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24 results for “renewable electricity”
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen (Dataset)
<p>This dataset contains central input assumptions and results related to the publication "Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen".</p> <p>Result files are contained in the <strong> results.zip</strong> archive file. The file contains for each scenario, as indicated by the folder structure, the following files:</p> <ul> <li><strong>results.csv</strong>: Central scenario results exported as <em>character separated value</em> <em>(csv)</em> file, with a semicolon (<strong>;</strong>) as field separator. All fields are quoted using double quotation marks <strong>"..."</strong>. Can be explored using standard office software like Microsoft Excel/Libre Office or other tools.</li> <li><strong>network.nc</strong>: PyPSA network file containing the optimized scenario with all input and unprocessed outputs (results). Can be explored using the <a href="https://pypsa.readthedocs.io">PyPSA software package</a>.</li> <li><strong>lcoes.csv</strong>: Levelised Cost of Electricity used to construct the renewable energy source (RES) based supply curve for each scenario.</li> </ul> <p>The dataset further contains the following files which represent central input assumptions to the model and scenarios, both as <em>CSV</em> files:</p> <ul> <li><strong>efficiencies.csv</strong>: Technology process and conversion efficiencies<em> </em>including more details on the assumptions and information on which references the assumptions are based.</li> <li><strong>costs_2030.csv</strong>: Technology cost assumptions for 2030 including more details on the assumptions and information on which references the assumptions are based. This data is based on this <a href="https://github.com/pypsa/technology-data">Technology Data repository</a> on GitHub.</li> </ul>
#energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany
<p>This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. Here is the tweet: https://twitter.com/WPSchill/status/1464368711817740298?s=20. And here is last year's tweet: https://twitter.com/WPSchill/status/1336633040676720640?s=20</p> <p>I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill</p>
Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles - summary results
<p>Summary results for submitted article: Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles</p> <p> </p>
Evolution of the price of electricity and the renewable energy production in Spain
<p>The dataset contains the evolution of electricity prices in Spain for the period 2020-11-01 to 2022-10-31. The average energy prices (in MWh) as a function of the market, the produced energy (in MW), as well as the renewable energy produced (in MW) by type (wind, solar, hydroelectric, etc.) are provided with a granularity of hours for the period of time mentioned above.</p> <p>The data has been obtained from the webpage: <a href="https://www.esios.ree.es/es/">https://www.esios.ree.es/es/</a>.</p> <p><strong>Disclaimer: </strong>Express consent was provided by Red Eléctrica de España (source and proprietary of the data) to gather the data using web scraping under the framework of a practicum from the Master in Data Science from the Universitat Oberta de Catalunya (UOC). Under no circumstance do they support the reuse of the data. We express our intention to use the data for the purpose of the activity and decline any commercial interest in the use of the data extracted.</p> <p>The data is published under a license: <strong>CC BY-NC-SA 4.0.</strong></p>
Renewables Projection with Storage in the Brazilian Electricity Matrix using OSeMOSYS and Flextool - Dataset
<p>This dataset presents the input and results for the study on the increase of Solar and Wind energy generation in the Brazilian Power System, as a way to achieve the NetZero by 2050. This study was conducted using the OSeMOSYS and Flextool as a deliverable of the Energy Modelling Platform for Latin America and The Caribbean Course - EMP-LAC 2023.</p>
SWAT+ simulation result used in "Smart renewable electricity portfolios in West Africa"
<p>Shapefiles and simulation results from SWAT+ used for the paper "Smart renewable electricity portfolios in West Africa" by Sterl et al., who used it to infer the interannual variability of river flow seasonality across West Africa.</p>
Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network
<p>Supplementary Data</p> <p><em>The Benefits of Cooperation in a Highly Renewable European Electricity Network</em><br> <em>doi:10.1016/j.energy.2017.06.004</em><br> <em>arXiv:1704.05492</em></p> <p>The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper.</p> <p>You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper <em>doi:10.1016/j.energy.2017.06.004</em>.<br> Please note that the derivation of the data in data/renewables/ is not open, because it uses the REatlas software [7] which has a closed source server part. (There is an free software implementation of the REatlas at https://github.com/FRESNA/atlite but it wasn't ready in time to be used for this dataset.)</p> <p>The code that is required to generate the output data consists of</p> <ul> <li>the python code opt_ws_network.py that builds and runs the PyPSA [0] model</li> <li>a SLURM script parameter_batch.py to run the model with different parameters</li> <li>a YAML file options.yml with the default parameter settings</li> </ul> <p>The code heavily relies on the python package <em>vresutils</em> which is available at https://github.com/FRESNA/vresutils</p> <p>The record also contains the input data in the data/ directory. They are described in detail in the paper, but a short summary is provided here:</p> <ul> <li><strong>costs</strong>: cost and other input parameter assumptions, see <em>Table 1</em> in the paper.</li> <li><strong>graph</strong>: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].</li> <li><strong>hydro</strong>: hydro generation data provided by the Restore2050 project [3] <ul> <li>inflow/: contains a csv files with daily inflow data for each country</li> <li>emil_hydro_capas.csv: country-scale power and energy capacity</li> <li>ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]</li> </ul> </li> <li><strong>load</strong>: hourly country-scale consumption for 2011 from ENTSO-E [5]</li> <li><strong>renewables</strong>: generation potentials for the renewable technologies onshore wind, offshore wind, and solar per country based on historic weather data [6]. The jupyter-notebook europe_renewables_potentials.ipynb describes the data generation and uses the REatlas software [7] which has open-source client but closed-source server software. The used cutout can therefore not be made available here, but is solely based on data from [8]. The processed data are in: <ul> <li>store_p_nom_max/: installation potential per technology per region</li> <li>store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region</li> </ul> </li> </ul> <p>The output data generated by the model is in sub-folders of the results/ directory following the naming scheme [costsource]-CO[CO2costs]-T[timerange]-[technologies]-LV[linevolume]_c[crossover]_base_[costsource]_solar1_7_[formulation]-[startdate]/, where</p> <ul> <li>costsource = diw2030</li> <li>CO2costs = 0</li> <li>timerange = 1_8761</li> <li>technologies = wWsgrpHb</li> <li>linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)</li> <li>crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)</li> <li>formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')</li> <li>startdate = time the optimisation was started</li> </ul> <p>Footnotes</p> <p>[0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307</p> <p>[1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015)</p> <p>[2] ENTSO-E, Indicative values for Net Transfer Capacities (NTC) in Continental Europe. European Transmission System Operators, 2011, https://www.entsoe.eu/publications/market-reports/ntc-values/ntc-matrix/Pages/default.aspx, accessed Jul 2014.</p> <p>[3] A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann, Simulation of renewable feed-in for power system studies, RESTORE 2050 project report, https://doi.org/10.5281/zenodo.804244</p> <p>[4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show</p> <p>[5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package</p> <p>[6] D. Heide, M. Greiner, L. Von Bremen, C. Hoffmann, Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36 (9) (2011) 2515–2523. https://doi.org/10.1016/j.renene.2011.02.009</p> <p>[7] G. B. Andresen, A. A. Søndergaard, M. Greiner, Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis, Energy 93, Part 1 (2015) 1074 – 1088. https://doi.org/10.1016/j.energy.2015.09.071</p> <p>[8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1</p>
Codes for the article: Biodiversity Impacts of Norway's Renewable Electricity Grid
<p>This repository contains the codes required to run the LCIA models and reproduce the results. The models are described in the paper "Biodiversity Impacts of Norway’s Renewable Electricity Grid" (https://doi.org/10.1016/j.jclepro.2024.143096).</p>
Modeling and simulation of a new Urban Lightweight Electric Vehicle concept based on the optimized use of renewable energies and the reduction of CO2 emissions
<p>This work has produced a series of scientifc contributions. This library develops different mathematical expressions and assumptions for the dynamic modelling of an smart-grid located within a solar-powered ULEV are derived. The code was developed using Dymola</p>
Result data related to "Tröndle (2020) -- Supply-side options to reduce land requirements of fully renewable electricity in Europe"
<p>The dataset contains the optimisation model and aggregated result data of my study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T. (2020). Supply-side options to reduce land requirements of fully renewable electricity in Europe. PLOS ONE.</p> </blockquote>
Non-Profit Energy Cooperatives as the Catalyst of the Movement of People to Renewable Electricity
<p>The Environment, Energy and Natural Resources (EENR) Center of University of Houston Law Center hosted a webinar on April 3rd – 11:00 am EDT as part of the Energy Transition Governance Project Webinar/Lecture Series.</p> <p>The webinar features: Melissa K. Scanlan, Visiting Professor, Boston College Law School, Director, New Economy Law Center, Law Professor, Vermont Law School;</p> <p>Gabe Pacyniak, Assistant Professor of Law, University of New Mexico;</p> <p>Aubin Nzaou, Marie Sklodowska-Curie Fellow in Law and Energy Policy, University of Houston Law Center.</p> <p>The renewable energy revolution poses a once in a lifetime opportunity for co-benefits of democracy, bringing electricity to those who lack it, and equity. This transition has the potential for widely shared prosperity, not just a decrease in suffering. To unlock that potential, however, we also need to disrupt business as usual in the dominant business model. The co-benefits are more likely if the institutions that lead the way are democratically owned and managed with the explicit goal of bringing the benefits of ownership to those who have been traditionally marginalized by the current economic and energy system. The talk will sketch the pathways to sustainable development and deep decarbonization of energy at the global level. First, Professor Melissa Scanlan will discuss research on non-profit energy cooperatives she is leading, which demonstrates how different aspects of the transition are already underway. Each case study, from a comparative perspective of Spain and the U.S., includes an overview of the cooperative’s history, type, and economic sector; context based on its industry and country; best practices in environmental sustainability; and its governance and cooperative legal structure. The panel will discuss Spanish renewable energy cooperatives involved in various points in the energy system and a US electricity cooperative in the politically conservative Deep South that is leading the transition to renewables. Second, Professor Gabe Pacyniak will discuss how the rural electricity cooperative model in the United States also presents some challenges when it comes to shifting to a lower-carbon electricity grid. The over 900 electric cooperatives in the United States have generally been slower to shift to lower-carbon electricity in comparison to other types of utilities. Pacyniak will share research that identifies structural and institutional factors for this lag, and will also highlight opportunities to strengthen cooperative supports and oversight to accelerate the shift to a low-carbon electricity system.</p>
Open Data Set for the article Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 2024, 6 (2), 363-382
<p>The datasets available for open access from the article ‘<em>Ballester, C. and Furió, D. (2024). Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 6 (2), 363-382</em>’ are provided here. This study has been supported by funding from the Spanish Ministry of Science, Innovation, and Universities (Project PGC2018-093645-B-100).</p> <p>The data encompasses the price series of the components of the final wholesale prices, other than the day-ahead market price, at an hourly frequency, from January 2017 to December 2021. In particular, the daily average of the hourly price series of the intraday market, which captures the net effect of the six sessions of the intraday market on the final price, the daily average of the hourly net effect on the final price of the procedure to solve technical constraints, the daily average of the hourly costs resulting from ancillary services and deviation management, the daily average of the hourly costs related to capacity payments and the daily average of the hourly costs associated with the interruptibility service. In addition, we compute the daily average of the hourly series of bids (price and amount) individually submitted by market participants to buy or sell energy, distinguishing between matched and non-matched bids, both in the day-ahead market and in the first session of the intraday market. Other energy-related price series included in the analysis are: the Dutch TTF futures price, the API2 index for the coal price, the EUA futures price, the percentage of hours with 100% use from the France-Spain interconnection, the spread from the France-Spain interconnection, the percentage of water reserves in the reservoirs of the Iberian Peninsula. All shareable data are made available in accordance with open data principles to promote transparency and reproducibility in research. However, there is a specific dataset that we are not authorized to share publicly. Specifically, this includes the data corresponding to the Dutch TTF futures price and the API2 index. Consequently, this dataset is published under restricted access.</p>
Online companion -Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids
<p>Online companion for the article "Technical Impacts of the Deployment of Renewable Energy Community on Electricity Distribution Grids".</p>
Data for Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market
<p>This page contains the datasets and codes used to generate the figures for the article Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market.</p> <p>Below you will find two datasets (.dta) and four codes (.do) files. Please note that the .do files contain the original paths to where the datasets were saved, you should change them to where they are saved in your computer. </p> <ol> <li>The file <em>clustering_prer_ok.do</em> uses dataset <em>yearlyvars_raw.dta</em> to generate three additional datasets <em>clusters.dta</em>, <em>yearly_scenarios.dta</em> and <em>xwalk.dta</em> <ul> <li><em>clusters.dta</em> is used in <em>Figs_2-5-6_ok.do</em></li> <li><em>yearly_scenarios.dta</em> is used in <em>Figs_3_ok.do</em></li> </ul> </li> <li>Dataset <em>hprice_raw.dta</em> is used together with the generated <em>xwalk.dta</em> in <em>Fig_4_ok.do</em>.</li> </ol>
Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts of Renewable Energy Penetration in California
Open the record for dataset details and reuse information.
Dataset for "Understanding New Zealand's Wind Resources as a Route to 100% Renewable Electricity"
<p>This package contains the simulated power output from individual wind farms in New Zealand, at half-hourly resolution over the period 1995 to 2016. This data underpins the paper “Understanding New Zealand’s Wind Resources as a Route to 100% Renewable Electricity”, submitted to Renewable Energy.</p> <p>The three data files contain the hourly time-series of simulated capacity factors for individual wind farms in New Zealand. They give the capacity factors for existing, proposed and suggested wind farms, which are defined in the paper named above. Metadata on these farms is given in the fourth file.</p> <p>Capacity factors are given in the range of [0, 1]. Two timestamps are given, in Greenwich Mean Time and local time (which switches between New Zealand Standard Time and New Zealand Daylight Time).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.